Method and system for evaluating and predicting bearing capacity change of regional water environment

Abstract

The invention discloses a regional water environment bearing capacity change evaluation and prediction method and system, and the method comprises the steps: obtaining water resource data, water environment data and social economic data in a target region, and constructing a water environment bearing capacity index system; the index system comprises a plurality of subsystems; evaluating the subsystems through a projection pursuit model based on a sparrow algorithm to obtain subsystem evaluation results; obtaining a comprehensive evaluation result of the water environment bearing capacity through an entropy weight approximation ideal solution sorting algorithm according to the subsystem evaluation result; constructing a Markov transition probability matrix according to the comprehensive evaluation result; and predicting the change probability of the water environment bearing capacity according to the Markov transition probability matrix. The method can improve the evaluation and prediction accuracy of the water environment bearing capacity change, and can be widely applied to the technical field of water environment evaluation and prediction.

Description

Technical Field

[0001] The present invention relates to the technical field of water environment evaluation and prediction, and in particular to a method and system for evaluating and predicting changes in regional water environment carrying capacity. Background Art

[0002] Water environment carrying capacity is an important indicator to measure the coordinated development degree of regional social and economic development and water resources environment. The existing water environment carrying capacity evaluation methods do not consider the influence of multiple variables and large time span on the index weight, and the evaluation process is prone to falling into local optimum. Most methods are limited to trend analysis and prediction on the time scale, and there are few studies on the prediction and simulation of the spatial pattern of water environment carrying capacity. Summary of the Invention

[0003] In view of this, the main purpose of the embodiments of the present invention is to provide a method and system for evaluating and predicting changes in regional water environment carrying capacity, in order to solve at least one of the existing technical problems. The present invention can improve the accuracy of evaluating and predicting changes in water environment carrying capacity.

[0004] To achieve the above object, on the one hand, an embodiment of the present invention provides a method for evaluating and predicting changes in regional water environment carrying capacity, the method comprising:

[0005] Obtain water resource data, water environment data and social and economic data within the target area, and construct an index system for water environment carrying capacity; the index system includes several subsystems;

[0006] Evaluate each of the subsystems through a projection pursuit model based on the sparrow algorithm to obtain subsystem evaluation results;

[0007] According to the subsystem evaluation results, obtain the comprehensive evaluation result of the water environment carrying capacity through the entropy weight technique for order preference by similarity to an ideal solution (TOPSIS) algorithm;

[0008] According to the comprehensive evaluation result, construct a Markov transition probability matrix;

[0009] Predict the change probability of the water environment carrying capacity according to the Markov transition probability matrix.

[0010] In some embodiments, the evaluating each of the subsystems through a projection pursuit model based on the sparrow algorithm to obtain subsystem evaluation results includes the following steps:

[0011] Obtain the original data of the index system;

[0012] Perform normalization processing on the original data to obtain standardized data;

[0013] Obtain the target projection direction through a projection pursuit model based on the sparrow algorithm;

[0014] Based on the target projection direction and the standardized data, a target projection value is obtained;

[0015] Wherein, the target projection value is the subsystem evaluation result.

[0016] In some embodiments, the obtaining of the comprehensive evaluation result of the water environment carrying capacity according to the subsystem evaluation result through the entropy weight technique for order preference by similarity to ideal solution (TOPSIS) algorithm includes the following steps:

[0017] Establish an evaluation criterion for the water environment carrying capacity;

[0018] According to the subsystem evaluation result, obtain the similarity degree to the ideal solution through the entropy weight TOPSIS algorithm;

[0019] According to the evaluation criterion and the similarity degree to the ideal solution, obtain the comprehensive evaluation result of the water environment carrying capacity.

[0020] In some embodiments, the obtaining of the similarity degree to the ideal solution according to the subsystem evaluation result through the entropy weight TOPSIS algorithm includes the following steps:

[0021] Obtain the proportion of the target sample in the indicators of the subsystem;

[0022] According to the proportion of the indicators, obtain the information entropy of the indicators;

[0023] According to the information entropy of the indicators, obtain the first weight of the subsystem;

[0024] According to the first weight, construct a weighted normalization matrix;

[0025] According to the weighted normalization matrix, obtain the positive ideal solution and the negative ideal solution of the water environment carrying capacity;

[0026] Obtain the positive distance between the subsystem evaluation result and the positive ideal solution, and obtain the negative distance between the subsystem evaluation result and the negative ideal solution;

[0027] According to the positive distance and the negative distance, obtain the similarity degree to the ideal solution.

[0028] In some embodiments, the constructing of the Markov transition probability matrix according to the comprehensive evaluation result includes the following steps:

[0029] According to the comprehensive evaluation result, divide the water environment carrying capacity into several state types;

[0030] Obtain the transition probability of each state type of the water environment carrying capacity;

[0031] Construct a Markov transition probability matrix according to the said transition probability.

[0032] In some embodiments, the formula used to obtain the transition probability of each state type of the water environment carrying capacity includes:

[0033]

[0034] where P cv represents the transition probability of transitioning to state type v at the next moment under the condition of being in state type c at the current moment; z cv represents the total number of target areas transitioning from state type c to state type v; z c represents the total number of target areas in state type c at the current moment.

[0035] To achieve the above object, on the other hand, an embodiment of the present invention provides an evaluation and prediction system for the change of regional water environment carrying capacity, and the system includes:

[0036] A first module, configured to obtain water resource data, water environment data, and social and economic data within a target area, and construct an index system for the water environment carrying capacity; the index system includes several subsystems;

[0037] A second module, configured to evaluate each of the subsystems through a projection pursuit model based on the sparrow algorithm to obtain subsystem evaluation results;

[0038] A third module, configured to obtain a comprehensive evaluation result of the water environment carrying capacity through an entropy weight technique for order preference by similarity to an ideal solution (TOPSIS) algorithm according to the subsystem evaluation results;

[0039] A fourth module, configured to construct a Markov transition probability matrix according to the comprehensive evaluation result;

[0040] A fifth module, configured to predict the change probability of the water environment carrying capacity according to the Markov transition probability matrix.

[0041] To achieve the above object, on the other hand, an embodiment of the present invention provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, it implements the above-mentioned evaluation and prediction method for the change of regional water environment carrying capacity.

[0042] To achieve the above object, on the other hand, an embodiment of the present invention provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the above-mentioned evaluation and prediction method for the change of regional water environment carrying capacity.

[0043] To achieve the above object, another aspect of the embodiments of the present invention provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. The processor of the computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to enable the computer device to execute the foregoing method for evaluating and predicting the change of regional water environment carrying capacity.

[0044] The embodiments of the present invention at least include the following beneficial effects: The present invention provides a method and a system for evaluating and predicting the change of regional water environment carrying capacity. The solution constructs an index system of water environment carrying capacity by obtaining water resource data, water environment data, and socioeconomic data in a target area; the index system includes several subsystems; the projection pursuit model based on the sparrow algorithm is used to evaluate each of the subsystems to obtain the evaluation results of the subsystems; according to the evaluation results of the subsystems, the entropy weight technique for order preference by similarity to an ideal solution (TOPSIS) algorithm is used to obtain the comprehensive evaluation result of the water environment carrying capacity; according to the comprehensive evaluation result, a Markov transition probability matrix is constructed; according to the Markov transition probability matrix, the change probability of the water environment carrying capacity is predicted, which can improve the accuracy of the evaluation and prediction of the change of water environment carrying capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0046] Figure 1 It is a flowchart of a method for evaluating and predicting the change of regional water environment carrying capacity provided by the embodiments of the present invention;

[0047] Figure 2 It is a schematic diagram of the processing flow of a method for evaluating and predicting the change of regional water environment carrying capacity provided by the embodiments of the present invention;

[0048] Figure 3 It is a schematic diagram of the hardware structure of an electronic device provided by the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0049] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the embodiments of the present invention. They are only examples of devices and methods consistent with some aspects of the embodiments of the present invention as detailed in the appended claims.

[0050] It should be noted that although functional modules are divided in the system schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order from the module division in the system or the order in the flowchart. The terms "first / S100" and "second / S200" in the specification, claims and the above-mentioned drawings may be used herein to describe various concepts, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of the present invention, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "when" as used herein may be interpreted as "when" or "while" or "in response to determining".

[0051] The terms "at least one", "a plurality of", "each", "any one" and the like used in the present invention, at least one includes one, two or more than two, a plurality of includes two or more than two, each refers to each one of the corresponding plurality, and any one refers to any one of the plurality.

[0052] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used herein are only for the purpose of describing the embodiments of the present invention and are not intended to limit the present invention.

[0053] Before the embodiments of the present invention are described in detail, some nouns and terms involved in the embodiments of the present invention will be described first. The nouns and terms involved in the embodiments of the present invention are applicable to the following explanations.

[0054] Water environment carrying capacity: It refers to the support capacity of the water environment for human activities and the development of the economic society in a certain area under a specific environmental state at a certain time. This concept is developed on the basis of carrying capacity and water environment capacity. Similar to water resources carrying capacity, the object of characterization is the natural water system. The difference is that water resources carrying capacity focuses on reflecting the requirements of the water environment in terms of "water volume", that is, the ability of the water body to supply water resources, while water environment carrying capacity is a comprehensive concept that comprehensively reflects the ability of the natural water system to provide water resources supply, water environment pollution absorption and water ecological service functions.

[0055] Projection pursuit model: Projection pursuit (abbreviated as PP) is a new and valuable technology developed by the international statistical community in the mid-1970s. It is an interdisciplinary subject of statistics, applied mathematics and computer technology. It is a new statistical method used to analyze and process high-dimensional observation data, especially non-normal and non-linear high-dimensional data. It projects high-dimensional data onto a low-dimensional subspace to find projections that can reflect the structure or characteristics of the original high-dimensional data, so as to achieve the purpose of studying and analyzing high-dimensional data. It has the advantages of robustness, anti-interference and high accuracy, so it has been widely used in many fields.

[0056] With the rapid development of social economy, problems such as water resource shortage, water environment pollution and water ecological damage have become more prominent. In many areas, the pressure exerted by human activities on the water environment has far exceeded the threshold that the local water environment can support. As a result, the functions of the watershed (regional) water system cannot be normally exerted, which not only directly affects the normal production and life of humans, but also restricts the coordinated and sustainable development of the social economy and the water environment. Water environment carrying capacity is an important indicator to measure the degree of coordinated development between regional social and economic development and water resources environment. The existing water environment carrying capacity evaluation methods do not consider the influence of multiple variables and large time span on the index weights, and the evaluation process is prone to fall into local optimum. Most methods are limited to the trend analysis and prediction of the time scale, and there are few studies on the prediction and simulation of the spatial pattern of water environment carrying capacity.

[0057] In view of this, as Figure 1 shown, the embodiment of the present invention provides an evaluation and prediction method for the change of regional water environment carrying capacity, and the method may include but is not limited to steps S100 to step S500:

[0058] Step S100, obtaining water resource data, water environment data and social economic data in the target area, and constructing an index system for water environment carrying capacity; the index system includes several subsystems;

[0059] Step S200, evaluating each of the subsystems through a projection pursuit model based on the sparrow algorithm to obtain subsystem evaluation results;

[0060] Step S300: Obtain the comprehensive evaluation result of the water environment carrying capacity through the entropy weight technique for order preference by similarity to ideal solution (TOPSIS) algorithm according to the subsystem evaluation result.

[0061] Step S400: Construct a Markov transition probability matrix according to the comprehensive evaluation result.

[0062] Step S500: Predict the change probability of the water environment carrying capacity according to the Markov transition probability matrix.

[0063] In step S100 of some embodiments, by collecting relevant data on water resources, water environment, social economy, etc. in the target area, a water resources subsystem is constructed based on the water resources data, a water environment subsystem is constructed based on the water environment data, and a social economy subsystem is constructed based on the social economy data. Through the water resources subsystem, the water environment subsystem, and the social economy subsystem, a water environment carrying capacity index system is initially constructed (as shown in Table 1). Among them, the index data in the water resources subsystem may include, but are not limited to, water production modulus, annual rainfall, total water supply, per capita water consumption, per capita water resources, agricultural water consumption, and agricultural water use; the index data in the water environment may include, but are not limited to, total wastewater discharge, urban sewage treatment rate, harmless treatment rate of domestic waste, green coverage rate of built-up areas, per capita park green area, and ecological environment water consumption; the index data in the social economy may include, but are not limited to, per capita GDP, population density, urbanization rate, proportion of the tertiary industry, natural population growth rate, water consumption per 10,000 yuan of GDP, ratio of per capita domestic water consumption of urban and rural residents, and average water consumption per mu of farmland irrigation.

[0064] Table 1

[0065]

[0066] In some embodiments, step S200 may include, but is not limited to, steps S210 to S240:

[0067] Step S210: Obtain the original data of the index system.

[0068] Step S220: Perform normalization processing on the original data to obtain standardized data.

[0069] Step S230: Obtain the target projection direction through a projection pursuit model based on the sparrow algorithm.

[0070] Step S240: Obtain the target projection value according to the target projection direction and the standardized data.

[0071] Among them, the target projection value is the subsystem evaluation result.

[0072] In step S200 of some embodiments, according to the water environment carrying capacity index system, by using the projection pursuit model improved based on the sparrow algorithm, the water resources - water environment - social economy three subsystems of the water environment carrying capacity are evaluated, and the subsystem evaluation results can be obtained.

[0073] In steps S210 to S220 of some embodiments, the original index data of the water environment carrying capacity index system is obtained, and the original index data is normalized to obtain the standardized data. The calculation formula is as follows:

[0074]

[0075] In the formula, u(x ij ) is the standardized data matrix; x ij is the original data matrix, representing the original value of the jth index in the ith region; max(x ij ) and min(x ij ) respectively represent the maximum and minimum values of the jth index. Among them, the indexes positively correlated with the evaluation results adopt The indexes negatively correlated with the evaluation results adopt

[0076] In steps S230 to S240 of some embodiments, through projection pursuit, the p-dimensional data {u ij |j = 1, 2, 3,..., p} is transformed into a one-dimensional projection value Z(i) with a(j) as the projection direction. The calculation formula is as follows:

[0077]

[0078] Among them, a(j) is a unit length vector; u ij is the standardized value in the standardized data matrix u(x ij ), representing the standardized value of the jth index in the ith region.

[0079] Optionally, the expression of the projection pursuit objective function max Q(a) is set as follows:

[0080] max Q(a) = S z ·D z

[0081]

[0082] Among them, S z is the standard deviation of the projection value Z(i); E(Z) is the average value of the comprehensive projection value Z(i); n is the number of samples; D zis the local density of the projection value Z(i); g(t) is the unit step function, and the intermediate variable t is used to represent R - r(i,j); r(i,j) is the distance between samples, r(i,j) = |Z(i) - Z(j)|; R is the radius of the local density.

[0083] Set the constraint condition as Then, the problem of searching for the optimal projection direction can be transformed into finding the corresponding a(j) when the projection pursuit objective function takes the maximum value. The novel swarm intelligence optimization algorithm, the Sparrow Search Algorithm (SSA), can be used to calculate the optimal target projection direction a(j). * , substitute the target projection direction a(j) * value into for a(j), then the target projection value Z(i) can be obtained, and this target projection value is the evaluation result of different subsystems of the water environment carrying capacity. Exemplarily, the steps and example code of the Sparrow Search Algorithm (SSA) and the Projection Pursuit (PP) model are as follows:

[0084]

[0085]

[0086]

[0087] In some embodiments, step S300 may include but is not limited to steps S310 to S330:

[0088] Step S310, establish the evaluation criteria for the water environment carrying capacity;

[0089] Step S320, according to the subsystem evaluation results, obtain the similarity degree of the ideal solution through the entropy weight TOPSIS algorithm;

[0090] Step S330, according to the evaluation criteria and the similarity degree of the ideal solution, obtain the comprehensive evaluation result of the water environment carrying capacity.

[0091] In step S310 of some embodiments, the water environment carrying capacity is divided into several levels, and the evaluation criteria for the water environment carrying capacity are established. Optionally, the water environment carrying capacity is divided into 4 levels, and the evaluation criteria for the water environment carrying capacity as shown in Table 2 can be obtained.

[0092] Table 2

[0093] Water environment carrying capacity Judgment level Level description [0，0.2) Low level Overload [0.2，0.4) Medium level Warning [0.4，0.6) Good Reasonable [0.6.1] High quality Excellent

[0094] In steps S320 to S330 of some embodiments, according to the subsystem evaluation results, the similarity degree of the ideal solution is obtained through the entropy weight TOPSIS algorithm, and then combined with the constructed evaluation criteria, the comprehensive evaluation result of the water environment carrying capacity can be obtained. Among them, according to the evaluation results of the three subsystems of the water environment carrying capacity, the entropy weight-TOPSIS method is used to calculate the comprehensive evaluation result of the water environment carrying capacity year by year. According to the established evaluation criteria of the water environment carrying capacity, the water environment carrying capacity of each year is divided into the corresponding state levels, and the longitudinal evolution evaluation of the water environment carrying capacity on the time scale within the region can be completed; by analogy to different regions, the state levels of the water environment carrying capacity between different regions are compared horizontally, and the evolution evaluation of the water environment carrying capacity on the spatial scale can be completed. Exemplarily, the steps of obtaining the similarity degree of the ideal solution through the entropy weight TOPSIS algorithm according to the subsystem evaluation results may include, but are not limited to, steps S321 to S327:

[0095] Step S321, obtaining the index proportion of the target sample in the subsystem;

[0096] Step S322, obtaining the index information entropy according to the index proportion;

[0097] Step S323, obtaining the first weight of the subsystem according to the index information entropy;

[0098] Step S324, constructing a weighted normalized matrix according to the first weight;

[0099] Step S325, obtaining the positive ideal solution and the negative ideal solution of the water environment carrying capacity according to the weighted normalized matrix;

[0100] Step S326, obtaining the positive distance between the subsystem evaluation result and the positive ideal solution, and obtaining the negative distance between the subsystem evaluation result and the negative ideal solution;

[0101] Step S327, obtaining the similarity degree of the ideal solution according to the positive distance and the negative distance.

[0102] In step S321 of some embodiments, calculating the index proportion of the target sample in the subsystem. Exemplarily, the proportion a ij of the i-th sample under the j-th subsystem in the subsystem is calculated by the following formula:

[0103]

[0104] In step S322 of some embodiments, obtaining the index information entropy according to the index proportion. Exemplarily, the index information entropy e j of the j-th subsystem is calculated by the following formula:

[0105]

[0106] In the formula, 0 < e j < 1; k is the number of target samples.

[0107] In step S323 of some embodiments, according to the index information entropy, the first weight of the subsystem is obtained. Exemplarily, the subsystem weight w j The calculation formula is:

[0108]

[0109] Among them, the weight w j represents the contribution degree of the jth subsystem to the water environment carrying capacity index system.

[0110] In step S324 of some embodiments, according to the subsystem weight w j , a weighted normalized matrix B is constructed. Exemplarily, the expression for calculating the weighted normalized matrix B is:

[0111] B = (w j x ij ) m×n = (b ij ) m×n

[0112]

[0113] Among them, b ij is the normalized value of the ith sample on the jth evaluation index.

[0114] In step S325 of some embodiments, according to the weighted normalized matrix, the ideal solution B + and the negative ideal solution B - of the water environment carrying capacity of the sample object to be evaluated are determined, and there is the following expression:

[0115]

[0116] In step S326 of some embodiments, the positive distance D + between the subsystem evaluation result and the positive ideal solution is obtained, and the negative distance D - between the subsystem evaluation result and the negative ideal solution is obtained, and there is the following expression:

[0117]

[0118] In step S327 of some embodiments, according to the positive distance and the negative distance, the closeness degree between the subsystem evaluation result and the ideal solution can be calculated to obtain the ideal solution similarity CI i , and the calculation formula is as follows:

[0119]

[0120] Among them, CI i ∈[0, 1], and the CI i value determined by the entropy weight - Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method can be used to compare the advantages and disadvantages, and the larger the CI i value, the better. Combining with the evaluation standard of water environment carrying capacity, the water environment carrying capacity is divided into four states according to [0, 0.2), [0.2, 0.4), [0.4, 0.6), [0.6, 1] (as shown in Table 2). The larger the value, the better the state of the water environment carrying capacity represents.

[0121] In some embodiments, step S400 may include but is not limited to steps S410 to S430:

[0122] Step S410, according to the comprehensive evaluation result, divide the water environment carrying capacity into several state types;

[0123] Step S420, obtain the transition probability of each of the state types of the water environment carrying capacity;

[0124] Step S430, according to the transition probability, construct a Markov transition probability matrix.

[0125] In steps S410 to S430 of some embodiments, according to the comprehensive evaluation result, the water environment carrying capacity is divided into k state types, k = 1, 2, 3, 4, corresponding to different carrying capacity states. By establishing a 1×k matrix F t =[F 1,t , F 2,t , …, F k,t , which is used to store the state probability of the initial water environment carrying capacity in each region at time t. Then construct a Markov transition probability matrix M, where the elements of the Markov transition probability matrix are P cv , and there is the following calculation formula:

[0126]

[0127] In the formula, P cv represents the transition probability of transferring to state type v at the next moment under the condition of being in state type c at the current moment; z cv represents the total number of target regions transferred from state type c to state type v; z c represents the total number of target regions in state type c at the current moment.

[0128] In step S500 of some embodiments, according to the Markov transition probability matrix, the change probability of the water environment carrying capacity can be predicted, reflecting the specific probability of the regional water environment carrying capacity transferring from the initial level to excellent or low-level state levels.

[0129] Exemplarily, there is a Markov transition probability matrix M(k = 4) as shown in Table 3, where P 11 represents the probability that the water environment carrying capacity of the region is of type 1 (i.e., low-level state) at time t and still belongs to type 1 at time t + 1. If P 14 = 0, it means that there is no possibility for the water environment carrying capacity of type 1 (i.e., low-level state) to transfer to type 4 (i.e., high-quality state). If P 14 ≠ 0, it means that the water environment carrying capacity of type 1 (i.e., low-level state) transfers to the high-quality state of the water environment carrying capacity, and the transfer probability is P 14 .

[0130] Table 3

[0131]

[0132] As Figure 2 shown, taking the Z urban agglomeration as an example, it mainly includes: City A, City B, City C, City D, City E, City F, City G, City H, City J. The processing flow of the evaluation and prediction method for the change of the regional water environment carrying capacity in the embodiments of the present invention includes the following steps:

[0133] Step 1: Index system construction: Collect data on water resources, water environment, social economy, etc. in the research years of cities A - J in the research area, and initially construct a water environment carrying capacity index system. As shown in Table 1, the water environment carrying capacity evaluation index system should be constructed around three aspects: the supporting ability of the water resources system, the ecological environment conditions, and the social and economic development level.

[0134] Step 2: According to the water environment carrying capacity index system, use the projection pursuit model of the sparrow algorithm to evaluate the water resources - water environment - social economy three subsystems of the water environment carrying capacity of City A.

[0135] Step 3: According to the evaluation results of the three subsystems of the water environment carrying capacity, use the entropy weight - technique for order preference by similarity to ideal solution method to calculate the comprehensive evaluation result of the water environment carrying capacity of City A year by year. According to the established water environment carrying capacity evaluation standard, divide the water environment carrying capacity of each year into the corresponding state level to complete the longitudinal evolution evaluation of the regional water environment carrying capacity on the time scale; and so on for cities B - J, and horizontally compare the water environment carrying capacity state levels between different regions to complete the spatial scale evolution evaluation of the water environment carrying capacity.

[0136] Step 4: Construct a Markov transition probability matrix based on the evaluation results of the water environment carrying capacity of cities A-J. This reflects the specific probabilities of the water environment carrying capacity of cities A-J in the Z urban agglomeration transferring from the initial level to excellent or low-level status levels.

[0137] An embodiment of the present invention also provides an evaluation and prediction system for changes in regional water environment carrying capacity, which can implement the above-mentioned evaluation and prediction method for changes in regional water environment carrying capacity. The system includes:

[0138] The first module is used to obtain water resource data, water environment data, and socioeconomic data within the target area and construct an index system for water environment carrying capacity; the index system includes several subsystems;

[0139] The second module is used to evaluate each of the subsystems through a projection pursuit model based on the sparrow algorithm to obtain subsystem evaluation results;

[0140] The third module is used to obtain the comprehensive evaluation result of the water environment carrying capacity through the entropy weight TOPSIS algorithm according to the subsystem evaluation results;

[0141] The fourth module is used to construct a Markov transition probability matrix according to the comprehensive evaluation results;

[0142] The fifth module is used to predict the change probability of the water environment carrying capacity according to the Markov transition probability matrix.

[0143] It can be understood that the content in the above method embodiments is applicable to the system embodiments of the present invention. The functions specifically implemented in the system embodiments of the present invention are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those in the above method embodiments.

[0144] An embodiment of the present invention also provides an electronic device, which includes a processor and a memory. The memory stores a computer program, and when the processor executes the computer program, it implements the above-mentioned evaluation and prediction method for changes in regional water environment carrying capacity. The electronic device can be any intelligent terminal including a tablet computer, an in-vehicle computer, etc.

[0145] It can be understood that the content in the above method embodiments is applicable to the device embodiments of the present invention. The functions specifically implemented in the device embodiments of the present invention are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those in the above method embodiments.

[0146] Reference Figure 3 , Figure 3 schematically shows the hardware structure of an electronic device in another embodiment. The electronic device includes:

[0147] The processor 601 can be implemented in the form of a general - purpose CPU (Central Processing Unit), a micro - processor, an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention;

[0148] The memory 602 can be implemented in the form of a Read Only Memory (ROM), a static storage device, a dynamic storage device, or a Random Access Memory (RAM), etc. The memory 602 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 602 and are called by the processor 601 to execute an evaluation and prediction method for changes in the regional water environment carrying capacity provided in the embodiments of the present invention;

[0149] The input / output interface 603 is used to implement information input and output;

[0150] The communication interface 604 is used to implement communication interaction between this device and other devices. It can achieve communication through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.);

[0151] The bus 605 transmits information between various components of the device (such as the processor 601, the memory 602, the input / output interface 603, and the communication interface 604);

[0152] Among them, the processor 601, the memory 602, the input / output interface 603, and the communication interface 604 achieve communication connections with each other inside the device through the bus 605.

[0153] The embodiments of the present invention also provide a computer - readable storage medium. The computer - readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the above - mentioned evaluation and prediction method for changes in the regional water environment carrying capacity.

[0154] It can be understood that the content in the above - mentioned method embodiments is applicable to the embodiments of this storage medium. The functions specifically implemented by the embodiments of this storage medium are the same as those of the above - mentioned method embodiments, and the beneficial effects achieved are also the same as those of the above - mentioned method embodiments.

[0155] An embodiment of the present invention also provides a computer program product or a computer program. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the foregoing method for evaluating and predicting the change of regional water environment carrying capacity.

[0156] In summary, the method and system for evaluating and predicting the change of regional water environment carrying capacity according to the embodiments of the present invention have the following advantages:

[0157] In the embodiment of the present invention, a projection pursuit model is selected for calculation, and the sparrow algorithm, a new intelligent optimization algorithm, is comprehensively used for optimization. It can not only fully retain the information volume of the time dimension contained in the indicators, but also present the dynamic differences of the evaluation results. While improving the global exploration ability, it can avoid falling into local optima and can objectively and truly reflect the evaluation results. In the prediction of water environment carrying capacity, different from the static analysis of time series, the Markov chain can reflect the dynamic evolution process of the water environment carrying capacity state.

[0158] In some alternative embodiments, the functions / operations mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the functions / operations involved, two consecutively shown blocks can actually be executed substantially simultaneously or the blocks can sometimes be executed in the reverse order. In addition, the embodiments presented and described in the flowcharts of the present invention are provided by way of example for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operations and logical flows presented herein. Alternative embodiments are contemplated, where the order of various operations is changed and where sub-operations described as part of a larger operation are executed independently.

[0159] In addition, although the present invention is described in the context of functional modules, it should be understood that unless otherwise stated to the contrary, one or more of the functions and / or features may be integrated in a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It can also be understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. More precisely, considering the attributes, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the modules will be understood within the ordinary skills of an engineer. Therefore, those skilled in the art can implement the present invention as set forth in the claims without undue experimentation. It can also be understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0160] If the above-mentioned functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0161] The logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in combination with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in combination with an instruction execution system, apparatus, or device.

[0162] More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection parts (electronic devices) having one or more wirings, portable computer disk cartridges (magnetic devices), random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memories), optical fiber devices, and portable compact disc read-only memories (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, then editing, interpreting, or otherwise processing it as necessary, and then storing it in a computer memory.

[0163] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

[0164] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0165] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the claims and their equivalents.

[0166] The above has specifically described the preferred embodiments of the present invention, but the present invention is not limited to the described embodiments. Those skilled in the art can also make various equivalent deformations or substitutions without departing from the spirit of the present invention, and these equivalent deformations or substitutions are all included within the scope defined by the claims of the present invention.

Claims

1. An evaluation and prediction method for the change of regional water environment carrying capacity, characterized in that Including the following steps: Obtain water resource data, water environment data, and socioeconomic data within the target area, and construct an index system for water environment carrying capacity; the index system includes several subsystems; Evaluate each of the subsystems through a projection pursuit model based on the sparrow algorithm to obtain subsystem evaluation results; According to the subsystem evaluation results, use the entropy weight technique for order preference by similarity to ideal solution (TOPSIS) algorithm to obtain the comprehensive evaluation result of the water environment carrying capacity; Construct a Markov transition probability matrix based on the comprehensive evaluation result; Predict the change probability of the water environment carrying capacity according to the Markov transition probability matrix.

2. The evaluation and prediction method for the change of regional water environment carrying capacity according to claim 1, characterized in that The step of evaluating each of the subsystems through a projection pursuit model based on the sparrow algorithm to obtain subsystem evaluation results includes the following steps: Obtain the original data of the index system; Perform normalization processing on the original data to obtain standardized data; Obtain the target projection direction through a projection pursuit model based on the sparrow algorithm; Obtain the target projection value according to the target projection direction and the standardized data; Among them, the target projection value is the subsystem evaluation result.

3. The evaluation and prediction method for the change of regional water environment carrying capacity according to claim 1, characterized in that The step of obtaining the comprehensive evaluation result of the water environment carrying capacity according to the subsystem evaluation results through the entropy weight TOPSIS algorithm includes the following steps: Establish the evaluation criteria for the water environment carrying capacity; Obtain the similarity to the ideal solution according to the subsystem evaluation results through the entropy weight TOPSIS algorithm; Obtain the comprehensive evaluation result of the water environment carrying capacity according to the evaluation criteria and the similarity to the ideal solution.

4. The evaluation and prediction method for the change of regional water environment carrying capacity according to claim 3, characterized in that The step of obtaining the similarity to the ideal solution according to the subsystem evaluation results through the entropy weight TOPSIS algorithm includes the following steps: Obtain the index proportion of the target sample in the subsystem; Obtain the index information entropy according to the index proportion; Obtain the first weight of the subsystem according to the index information entropy; Construct a weighted normalization matrix according to the first weight; Obtain the positive ideal solution and negative ideal solution of the water environment carrying capacity according to the weighted normalization matrix; Obtain the positive distance between the subsystem evaluation result and the positive ideal solution, and obtain the negative distance between the subsystem evaluation result and the negative ideal solution; Obtain the similarity to the ideal solution according to the positive distance and the negative distance.

5. The evaluation and prediction method for the change of regional water environment carrying capacity according to claim 1, characterized in that, The step of constructing a Markov transition probability matrix according to the comprehensive evaluation result includes the following steps: Divide the water environment carrying capacity into several state types according to the comprehensive evaluation result; Obtain the transition probabilities of each of the state types of the water environment carrying capacity; Construct a Markov transition probability matrix according to the transition probabilities.

6. The evaluation and prediction method for the change of regional water environment carrying capacity according to claim 5, characterized in that The formula used for obtaining the transition probabilities of each of the state types of the water environment carrying capacity includes: Among them, P cv represents the transition probability of transferring to state type v at the next moment under the condition of being in state type c at the current moment; z cv represents the total number of target regions transferred from state type c to state type v; z c represents the total number of target regions in state type c at the current moment.

7. An evaluation and prediction system for the change of regional water environment carrying capacity, characterized in that, Including: The first module is used to obtain water resource data, water environment data, and socioeconomic data within the target area, and construct an index system for water environment carrying capacity; the index system includes several subsystems; The second module is used to evaluate each of the subsystems through a projection pursuit model based on the sparrow algorithm to obtain subsystem evaluation results; The third module is used to obtain the comprehensive evaluation result of the water environment carrying capacity through the entropy weight technique for order preference by similarity to ideal solution (TOPSIS) algorithm according to the subsystem evaluation result; The fourth module is used to construct a Markov transition probability matrix according to the comprehensive evaluation result; The fifth module is used to predict the change probability of the water environment carrying capacity according to the Markov transition probability matrix.

8. An electronic device, characterized in that, It includes a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The storage medium stores a program, and the program is executed by the processor to implement the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method according to any one of claims 1 to 6.

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